Final Project – Final Deliverable

Women Entrepreneurship

Introduction

Over the past couple of weeks, I have decided to embark on an exciting journey to explore the effects of women’s entrepreneurship worldwide, spanning both developed and underdeveloped countries. This interest initially arose from a brief curisotry behind women’s entrepreneurship. As I delved deeper, I stumbled upon a concept that was new to us: most women who decide to embark on the entrepreneurial journey typically come from lower socioeconomic classes. This was surprising to us, as I had always assumed the opposite.

Seeking to understand if there is a correlation between the pursuit of entrepreneurship and demographics such as gender, age, and socioeconomic status, I aim to gain insight into the following questions:

1. Are higher levels of development associated with higher educational attainment?
2. Is female necessity-driven entrepreneurship (FNE) more prevalent in countries with lower levels of development?
3. Do developed countries exhibit a higher individual perception of the entrepreneurship index?

Background: Datasets

I will be using two sources of data, our first dataset is quite large and was collected in 2014 by the Global Entrepreneurship Monitor (GEM), GEM it prides itself on integrity, innovation and excellence. It has collected data for the past 25 years across numerous countries, capturing useful demographics and data some among it being economics, and entrepreneurship. Our second dataset was published by The Global Entrepreneurship and Development Institute (GEDI). It focuses on three main areas: entrepreneurship, economic development, and prosperity. One of its more remarkable tools is the GEI index methodology that carefully analyses and measure the dynamics of entrepreneurial ecosystems throughout many levels: national, regional, and local. I selected the 2015 report.

Limitations

A large limitation would be the accuracy of the data I researched. Because I live in a very faced paced world and entrepreneurship is fast paced, it brings up questions regarding the accuracy behind our two datasets since they’re date a while back. Second, as I continued to investigate our source data I came across an important limitation our second data set collects data from GEM. This could indicate that our data overlaps in certain areas, which could potentially skew our results.

Sources

• https://www.gemconsortium.org/data/sets?id=aps
• http://thegedi.org/2015-female-entrepreneurship-index-press-release/

Questions with interactive visualizations

library(haven)
library(dplyr)
library(stringr)
library(ggplot2)
library(plotly)

#load GEM file
GEM_2014_individual.df <- read_sav("C:/Users/Sujin/Documents/University of Washington/INFO201/Group Project/GEM 2014 APS Global - Individual Level Data_9Mar.sav")

# load development file
Women_Entrepreneurship_and_Labor_Force.df <- read.csv("C:/Users/Sujin/Documents/University of Washington/INFO201/Group Project/Women Entrepreneurship and Labor Force.csv", sep=";")

# load country name connector list
country_names <- read.csv("C:/Users/Sujin/Documents/University of Washington/INFO201/Group Project/alpha-2 list.csv")

# pull out what I need; make smaller dataset
GEM_2014_df <-
  GEM_2014_individual.df |>
  select(ctryalp, GEMEDUC, TEAyyFNE, INDSUPyy) |> # select country and gemeduc
  group_by(GEMEDUC) 

# combine  
GEM_2014_df <- GEM_2014_df |>
  left_join(country_names, by = c("ctryalp" = "Code"))
combined_df <-
  GEM_2014_df |> #full_join(Women_Entrepreneurship_and_Labor_Force.df, by = c("Name" = "Country"))
  inner_join(Women_Entrepreneurship_and_Labor_Force.df, by = c("Name" = "Country"))

Level of Development vs. Education Attainment (GEMEDUC)

Are higher levels of development associated with higher educational attainment?

# group by level of dev and education
DevEdu_counts_df <- combined_df |>
  group_by(Level.of.development, GEMEDUC) |>
  summarise(count = n())

# Level of Development vs. Educational attainment
DevEdu <- ggplot(DevEdu_counts_df, aes(x = GEMEDUC, y = count, fill = Level.of.development)) +
  geom_bar(stat = "identity", color = "black", position = "dodge") +
  labs(title = "Level of Development vs. Educational attainment",
       x = "Level of Educational attainment",
       y = "Count",
       fill = "Level of Development") +
  theme_minimal()
ggplotly(DevEdu)

There is a clear trend that shows that entrepreneurs in developed countries also have a higher level of education attainment. This especially stands out in the first and last two bars of the graph.

Level of Development and Female Necessity-driven Entrepreneurship (TEAyyFNE)

Is female necessity-driven entrepreneurship (FNE) more prevalent in countries with lower development levels?

# group by level of dev and TEAyyFNE
DevTEA_counts_df <- combined_df |>
  group_by(Level.of.development, TEAyyFNE) |>
  summarise(count = n())

# Level of development vs. TEAyyFNE
DevTEA <- ggplot(DevTEA_counts_df, aes(x = TEAyyFNE, y = count, fill = Level.of.development)) +
  geom_bar(stat = "identity", color = "black", position = "dodge") +
  labs(title = "Level of Development vs. female Necessity-Driven Entrepreneurship (FNE)",
       x = "Female necessity-driven entrepreneurship",
       y = "Count",
       fill = "Level of Development") +
  theme_minimal()
ggplotly(DevTEA)

This graph reveals that female entrepreneurs who answered yes to their entrepreneurship being necessity-driven entrepreneurship are more prevalent in developing countries.

Individual perception of entrepreneurship index (INDSUPyy) and Level of development

Do developed countries have a higher individual perception of the entrepreneurship index?

# group by INDSUPyy and Entrepreneurship Index
INDSUPyy_counts_df <- combined_df |>
  group_by(Level.of.development, INDSUPyy) |>
  summarise(count = n())

# Individual perception of entrepreneurship index (INDSUPyy) vs. Level of development
INDSUPyyIndex <- ggplot(INDSUPyy_counts_df, aes(x = INDSUPyy, y = count, fill = Level.of.development )) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Individual perception of entrepreneurship index vs. Level of development",
       x = "Individual perception of entrepreneurship index",
       y = "Count",
       fill = "Level of development") +
  theme_minimal()
ggplotly(INDSUPyyIndex)

Individuals in developed countries have a lower perception of the entrepreneurship index, meaning that those in developing countries have a positive perception of entrepreneurship than those in developed countries.

Conclusion: Summary of Key Takeaways

Using data from the Global Entrepreneurship Monitor (GEM) and the Global Entrepreneurship and Development Institute (GEDI), this study investigated the connection between women’s entrepreneurship and socioeconomic characteristics. I investigated through the analysis how perception, necessity, and education affect entrepreneurial endeavors at different developmental stages. The three main conclusions drawn from our investigation are as follows:

1. Educational Attainment and Development Levels

Development levels and educational attainment among female entrepreneurs were shown to be clearly correlated. Higher educational attainment was more common among women in developed nations, underscoring the important role that educational access plays in encouraging entrepreneurship. In order to strengthen the basis for opportunity-driven entrepreneurship, this emphasizes the significance of educational policies and investments in emerging nations.

2. Necessity-Driven Entrepreneurship in Developing Nations

Our research revealed that necessity-driven entrepreneurship is more common in underdeveloped nations, where women are more likely to launch enterprises to meet their financial needs than to take advantage of commercial prospects. This highlights a significant lack of tools and support networks that could enable women to move from survival-based to growth-oriented economic endeavors. Potential measures to lessen financial limitations include microfinance, training initiatives, and network access.

3. Positive Entrepreneurial Perceptions in Developing Regions

It’s interesting to note that people in developing countries had a more favorable opinion of entrepreneurship than people in rich ones. This optimism may be a reflection of the spirit of entrepreneurship as a means of adaptability and resilience in difficult economic times. The entrepreneurial ecosystems in these areas may be strengthened by utilizing this enthusiasm through focused support initiatives.

Broader Implications

This study emphasizes how education, the state of the economy, and cultural viewpoints all influence women’s entrepreneurship. Policymakers must address systemic obstacles while leveraging the unique advantages of various regions in order to promote inclusive growth. Real-time and qualitative data should be incorporated into future research to further our comprehension of this dynamic environment.